A comprehensive evaluation of region-time-magnitude behaviours of aftershocks following the 24 January 2020 (Mw = 6.8) Elazığ-Sivrice (Türkiye) earthquake was achieved by using the characteristic parameters such as b-value, p-value, Dc-value and Mamax value of aftershock occurrences. The b-value was calculated as 0.82 ± 0.02 by considering the magnitude of the completeness value as Mcomp = 1.9, and it is relatively small compared to typical b ≈ 1 for the magnitude-frequency relationship of aftershocks. This low b-value may also be caused by the abundance of aftershocks with ML ≥ 4.0. The p-value was computed as 0.80 ± 0.02 with c-value = 0.279 ± 0.098 and is smaller than the global value of p ≈ 1. This low p-value may be due to a relatively slow decay rate of aftershock activity, and the modified Omori model seems appropriate for the estimation of decay parameters. The Dc-value was estimated as 1.87 ± 0.07. This large value shows that aftershocks are homogeneously distributed and more clustered at larger scales/in smaller areas. The temporal variation of b-value indicates that decreases in b-value may result from the gradual increase in the effective stress following the larger aftershocks. The lowest b-values and Mamax values greater than 5.0 were observed in the north, south and southwest parts of the mainshock including Pütürge and Erkenek segments. These results show that there is an apparent relation between the smallest b-values and the largest Mamax values. The largest p-values were estimated in and around the main shock including Pütürge segment. The regions with the smallest b-value and the largest p-value have high stress and coseismic deformation, respectively. Stress variations and coseismic deformation are extremely effective on the changes of b- and p-values. As a remarkable result, aftershock hazard following the mainshock may be considered extremely related to aftershock parameters, and detailed analyses of the region-time-magnitude characteristics of aftershocks are recommended for a preliminary evaluation following the mainshock.

The network of streams is one of the most active and dynamic factors reforming the relief and landscape. Together with the global factors of climate formation and trends of neotectonic movement, the streams are naturally and by means of the anthropogenic factor joining or re-joining the catchments the area of which is changeable. The aim of this research was to reveal in greater detail the development of the Lithuanian Baltic Sea coastal landscape with focus on Lake Kalotė outflows surface runoff within two coastal catchments, the catchment of Pajūris Rivers and the Akmena-Danė River sub-catchment, in time and space. That is important even nowadays under the climatic challenges and anthropogenic pressure in the coastal areas rich with lakes and small streams. The methods used were the analysis of old maps and aero-photo material, geological-geomorphological, hydrological, land reclamation and soil data obtained in the area of the Pajūris Regional Park and Klaipėda district municipality, supplemented with the results of recent land surface and soil surveys, drone technique and ArcGIS. As a result the possible scenarios of Lake Kalotė outflows surface runoff directions from the 17th to the beginning of the 21st century were prepared with the evidence of periods with lake‘s bifurcation regime

The main purpose of this study was to compare the performance and validation of six machine learning models (extreme gradient boosting, random forest, artificial neural network, support vector machine, C4.5 decision tree, and naive Bayes) in landslide susceptibility modelling. The province of Rize, which has the highest rate of landslide events in Türkiye, was chosen as the study area. The conditioning factors (distance to roads, lithology, drainage density, slope, topographic wetness index (TWI), soil depth, distance to rivers, land use, NDVI, plan curvature, elevation, aspect, profile curvature) affecting the landslide were determined using the ReliefF method. A total of 516 landslides were identified for creating models, comparing performance, and validating results. The performance and validation of the models were determined by the receiver operating characteristics (ROC), sensitivity, specificity, accuracy, and kappa index. The results show that the XGBoost model outperforms the other five machine learning models in terms of accuracy and performance and is the most effective model for generating landslide susceptibility maps in Rize (Türkiye).

This work is dedicated to Antanas Karolis Giedraitis (Antoni Karol Giedroyć, A.K. Gedroïz, А.K. Гедройц) (1848–1909) geological research that he did in Lithuania territory for geological mapping. In the History of Science, A.K. Giedraitis is called the first professional geologist of Lithuania, who investigated geology of our and neighboring countries. In 1895 he compiled a geological map of very large territory – Russian Empire governorates of Vilnius, Kaunas, Suvalkai, Gardinas, and Minskas according to international standards. It is important to understand A.K. Giedraitis’ approach to the formation of Quaternary sediments and the theory of polyglacialism. He took at that time the bold position that this entire region two or even three times had been covered by a glacier that advanced from the North. Based on the weathering (wearing) of erratic boulders and glacial incisions, he tried to describe the limits of glacier expansion and the directions of its movement. A.K. Giedraitis’ professional achievements are well-known, they are evidenced by the detailed reports and publications of his geological research in German, Polish, and Russian, which were published in 1886, 1887, and 1894. A summary of all his research was published in 1895, together with a geological map to a scale of 1:420 000. Interestingly, research carried out nowadays has confirmed some of the more than 130-year-old insights of A.K. Giedraitis about the geological formation of our country.

To assess aeolian relief dynamics in Pilkosios Dunes, 6 digital elevation models (DEMs) were prepared from the year 2010, for January, May and October of 2018, for May of 2019, and for November of 2022. These DEMs were then compared with each other to obtain elevation difference rasters through time. The obtained results indicated that elevation changes were directional through time, and their highest intensity coincided with the bare-sand surface class, where active blowout landforms were located. In these locations, the average negative elevation change rate was determined to be approximately 2 cm/year. To analyse the driving forces of these elevation changes, the relationships between a measure of elevation change and the tested factor characteristics were evaluated. The strongest relationship amongst all tested factors was found with the distance to the edge of grassland/bare sand. Locations that were farther away from this edge experienced a four times larger decrease in elevation, compared to areas closer to the edge. The distance to the shoreline, which is related to the absolute altitude, was also an important factor. This relationship can be summarized as follows: the lowest areas, which were further from the lagoon, were inactive; while the highest locations, which were closer to the shoreline, had the highest intensity of elevation change (averaging 0.5–0.7 m for adjacent to the shoreline locations and -1.7 m for locations ~ 300 m away from the shoreline). The slope factor described a trend of how the steepest slopes were decreasing in height by 2 m on average, while gentler slopes were mostly stable.

The development of the Fennoscandian Ice Sheet (FIS) in its SW Baltic Sea sector during MIS 2 led to the formation of the Jasmund Glacitectonic Complex (JGC). The evolutional stages of the latter reflect small-dimensioned ice sheet oscillations. A glacitectonically formed piggyback basin is in focus to give direct information on the evolution of the JGC. We present a set of five OSL ages from syn-kinematic glacilacustrine and alluvial fan sediments deposited between ice margin-parallel thrust-bounded ridges from the southern margin of the JGC. The ice front proximal depositional environment and short transport distances of the sediments implicate a possible insufficient bleaching of the luminescence signal. Nonetheless, we were able to gain reliable ages of ~22 ka for the growth of the southern JGC. The samples pre-date the Oder ice stream reaching its maximum extent, the Pomeranian ice marginal belt at ~18–20 ka. A comparison of our data with the existing age data related to the evolution of the JGC will contribute to a better understanding of the FIS dynamics in the research area and the timing of the Pomeranian phase in NE Germany.

In this study, the correlations between the heat flow density and seismotectonic b-value in the Elbasani area of Albania were investigated to understand the how low-velocity layers underneath the Elbasani area in central Albania struggling the large-scale Vlora-Lushnja-Elbasani-Dibra Fault Zone affect the heat flow data. For this purpose, the heat flow and regional distribution of b-value were imaged for different locations and depths. To achieve the analysis, the Albanian Seismological Catalogue for the period between 1 July 1968 and 26 December 2022, including 1830 earthquakes with a local magnitude of 0.5 ≤ Ml ≤ 5.2 that occurred at the depths below 70 kilometres, was considered. The b-value was calculated as 1.03 ± 0.06 by considering the magnitude of the completeness value as Mc-value = 2.6. This result shows that the b-value of earthquake distribution in the Vlora-Lushnja-Elbasani-Dibra (VLED) fault zone is well represented by the Gutenberg and Richter (G-R) scaling law with the b-value close to 1.0. The regional variations of the b-value show that b-values smaller than 0.9 were observed in the western and south-western parts of the Llinxha-Kozan thermal water belt. The depth distribution of the b-value indicates that there exists a sharp decrease in the b-values from 1.15 to 0.7 in the depths varying from 5 to 20 km. The highest heat flow values were observed on the Dumrea diapiric dome and in the central part of the Elbasani area. Thus, our analysis indicates significant and robust correlations between the geothermal and earthquake distribution. The discontinuity of Moho interface is deep in the regions where high b-values were observed. Low b-values are found at the depths ranging from 20–25 km and 35–40 km in the Dumrea evaporite massif area. We have evidence that high b-values and large heat flow values are related to the low seismic velocity layers underneath the Elbasani area. The low-velocity zone (LVZ) in Albania occurs in the Earth’s crust and in the upper mantle. It is characterized by an unusually low seismic shear wave velocity compared to the surrounding depth intervals. It is well known that the low-velocity layers in the Elbasani area are determined in the upper crust, at shallow depths of 2–4 km, and in the middle crust at a 10–14 km depth. Hence, it is suggested that the Moho interface in the eastern part of the Elbasani area is relatively deep (45 km) compared to the western part of Albania (35 km), and the magnitude of earthquakes is smaller where the high heat flow values were observed.

Serious stability issues could arise both during and after construction if the dams’ hull designs are not realistically accurate. Engineering studies are therefore crucial for identifying the body’s instability properties in designing the dam body. In this study, horizontal and vertical displacement and stress-deformation analyses were carried out on the body of the Midi Waste Dam located in Karamustafa Village of Gümüşhane Province. In these analyses, the elasticity modulus and Poisson’s ratio of the soil were determined using the seismic method, which is one of the commonly used geophysical methods. The cohesion and internal friction angle of the rock fill were determined by taking into account Leps’ charts. The state of the dam body under the influence of the siltation load and seismic load at the end of operation was shown by applying the finite element method. It was found that there was no instability issue with the researched dam because the safety numbers obtained from the stability study of the dam body were greater than the 1.2 safety number accepted for the stability of the dams (SRF: 2.03, SRF: 1.37). The maximum vertical displacement of the dam body at the foundation base was found as 3.78 cm, the horizontal displacement as 5.80 cm, and the total displacement amount as 6.75 cm when the dam body was examined in terms of displacements. In terms of the statics of the dam body, the vertical, horizontal, and total displacements estimated with the numerical analysis methods did not present a problem, and it was demonstrated that this scenario was also supported by the stability of the body in the applied analyses.

In regions susceptible to river freezing and flooding, river ice detection is a priority. Localization of ice jams and ice drift zones could mean a faster and better response to possible flooding areas, and classification of river ice could help better predict freezing and thawing conditions that hinder the use of commercial and recreational river transport. As many freezing-prone rivers are located in regions with short winter days and common cloud cover, the use of optical sensors can be very limited, therefore, the use of Synthetic Aperture Radar (SAR) – a microwave imaging radar – is more applicable. In this article, Sentinel-1 SAR C-band imagery is used to create derivate texture rasters, which are analyzed, compared with known optical imagery and then considered for river ice detection and discrimination. These results are compared in terms of their effectiveness for river ice discrimination, and the most useful methods are selected. The chosen methods are then compared in an experimental machine-learning model capable of detecting and classifying ice and water. Various machine-learning approaches (both classical and deep-learning) are considered and compared, and the best models are selected. The purpose of this research is to analyze the capability of texture rasters, calculated from a gray-level co-occurrence matrix (GLCM), to discriminate river ice. Texture rasters have recently been applied for river ice classification by de Roda Husman et al. (de Roda Husman et al. 2021), but included only three metrics. This research aims to expand on this knowledge by comparing eight metrics instead of three, as well as including an experiment with a deep-learning model. The results demonstrate that in machine-learning experiments, only one texture measure out of eight (GLCM Mean calculation) is able to discriminate river ice better than discrimination from a standard SAR backscatter intensity image (the baseline).

The Akçakale cave is a significant natural and cultural heritage site in the Black Sea region of eastern Turkey. The complex geometry and difficult-to-access areas of the cave have made the use of traditional mapping methods challenging. To overcome these limitations, this study utilized TLS and UAV technology to produce highly accurate 2D and 3D data for cave management and risk assessment purposes. The TLS system was used to create a detailed 3D point cloud of the cave interior, while the UAV system generated a 3D model of the surface topography outside the cave. The two sets of data were combined in the GIS environment using a geodetic network established in the study area, providing a common geodetic reference system for both TLS and UAV data. The study found that the cave area is 13,750 m2, which is smaller than the area of 18,000 m2 that was previously estimated using conventional measurement methods. The volume and ceiling heights of the cave were calculated using the elevation models generated from TLS point cloud data. The 3D point cloud data were also used to map dripstone locations on the floor and ceiling of the cave, and the boundaries of rock blocks on the ground were precisely determined. The study identified potential risks associated with the cave, particularly the risk of rockfall in the source rock areas around the cave entrance and the southern part of the cave. The nearest building to the cave is approximately 35 meters away, and all the buildings in the area are less than 300 meters from the cave. In the event of the cave collapse, the buildings in the southern part of the cave are at risk of rockfall. This study demonstrates the effectiveness of combining data from TLS and UAV systems to generate broad and sensitive cave mapping and risk assessment data, which are critical for cave management and safety. The collected data can be used for cave stability investigations and rockfall risk assessments. This study provides a foundation for future explorations of the Akcakale cave and highlights the potential for modern surveying techniques to enhance our understanding of complex geological structures such as caves.