弗莱贝格工业大学

Stochastic fracture network models are important tools for characterizing heterogeneity and seepage behavior in fractured rock masses in geotechnical engineering. To address the limitations of conventional models—poor generalization, high uncertainty in flow description, and susceptibility to distortion—this paper proposes a novel computational framework: a geometric-topologically constrained stochastic fracture network modeling method that accounts for the synergistic interplay between fracture geometry and topological connectivity. After validating the seepage reliability of the constrained model, uncertainty quantification analysis identifies the respective contributions of various geometric and topological parameters to fracture network connectivity efficiency. Furthermore, a practical modeling approach driven by dominant controlling factors is proposed. Results indicate that fractured rock samples exhibit pronounced dominant-flow characteristics. Fractures create preferential flow channels, accelerating gas migration from free space into the porous medium region. Uncertainty quantification reveals that the average number of connections per branch (CB), the average number of connections per fracture (CL), and the angle deviation of fracture (θ) are the core controlling factors governing system-scale seepage behavior. Fracture aperture (b), the number of X-type nodes (NX), and the number of fracture branches (NB) are identified as key influencing factors. Volumetric fracture intensity (P32) and the number of I-type nodes (NI) indirectly regulate seepage responses through interactions with other topological parameters. Based on these findings, a dominant-factor-driven modeling approach is proposed, which prioritizes accurate characterization of parameters controlling fracture network connectivity and the dominant seepage direction, while reasonably simplifying secondary factors that contribute less to seepage behavior. The fracture network model constructed using this new method achieves a consistency rate of approximately 96.6% with the reference model in terms of seepage behavior, demonstrating its robustness in effectively characterizing the baseline seepage response. These findings provide a practical solution for engineering-scale fractured rock mass modeling, offering a balance between accuracy and computational efficiency.

The Cheb Volcanic Field, part of the Central European Volcanic Province (CEVP), is an active long-lived monogenetic volcanic region composed of maar-diatreme structures and scoria cones. Although hidden volcanic edifices continue to be identified along the western flanks of the Cheb Basin, the northern sector—where the earliest activity is expected—remains poorly investigated. Consequently, the early geodynamic evolution at the intersection of the Regensburg–Leipzig Zone and the Eger (Ohře) Graben is still not well constrained. To address this gap, we acquired remote‑sensing and geophysical reconnaissance data to detect previously unrecognized volcanic dikes near Bärendorf in the southern Vogtland region. Erosional incision along the local stream exposed volcanic material from NE–SW‑striking dike, enabling direct examination and confirming its composition as coherent olivine‑melanephelinite with an amygdaloidal texture. The presence of well‑preserved volcanic glass and a mineral assemblage of peridotitic forsterite, Al‑rich clinopyroxene, and nepheline allows thermobarometric modeling, indicating lower crustal magma storage at ca. 25–40 km (corresponding to 7–12 kbar), consistent with present crustal thickness. A 40Ar/39Ar groundmass age of 30 ± 2 Ma identifies the Bärendorf maar as one of the oldest volcanic edifices in the Cheb Basin and Elstergebirge region. These findings suggest that mantle upwelling beneath the Cheb Volcanic Field has remained stable over an exceptionally long period, beginning in the late Paleogene. This indicates that this monogenetic volcanic field is among the most long‑lived worldwide.

The relationship between earthquakes and fluid pressure variations remains debated, particularly regarding whether surface fluid anomalies reflect pore pressure changes at seismogenic depth. Anomalous fluid emissions at the Chung-lun mud pool in southwestern Taiwan were continuously monitored between 2008 and 2010, with quantitative analyses focusing on periods of stable observations from mid-2009 to late 2010 using water level sensors and a digital camera system. Long-term anomalies were characterized by sustained variations in water levels and changes in degassing behavior that were independent of precipitation. In four out of five cases, these anomalies occurred in close temporal association with nearby earthquakes, indicating a systematic temporal relationship rather than a simple random coincidence. Short-term variations were linked primarily to rainfall events, while long-term discharge anomalies are interpreted as being consistent with pore pressure perturbations at depth, based on indirect surface observations and the exclusion of meteorological and shallow hydrological controls, rather than direct measurements within the seismogenic zone. The results hypothesize that pore fluid overpressure at depth can modulate both fault stability and surface fluid discharge, consistent with the concept of fault valve behavior. Some anomalies were recorded prior to seismic events, suggesting that gradual increases in pore pressure may contribute to stress evolution in critically stressed thrust faults, although the coupling is not deterministic. The integration of instrumental and photographic observations at Chung-lun demonstrates the potential of mud pools as natural observatories for geodynamically induced interactions between tectonic activity and fluid transport and emission. It highlights their important role as valuable components in regional earthquake monitoring networks.

Landslides are among the most damaging natural hazards, causing recurrent loss of life, infrastructure disruption, and socio-economic impacts in mountainous regions. Accurate landslide susceptibility mapping (LSM) is essential for hazard mitigation, yet robust regional-scale prediction remains challenging in geomorphically complex terrain. This study presents an integrated machine learning workflow for high-resolution LSM in the Upper Svaneti region of northern Georgia, a tectonically active and landslide-prone sector of the central Greater Caucasus. The framework combines exploratory geomorphic structure analysis with a spatially robust stacked ensemble and probabilistic uncertainty quantification. The approach integrates CatBoost, Random Forest, Extra Trees, and a multilayer perceptron within an XGBoost meta-learner trained on out-of-fold probabilities. Hyperparameters were optimized via successive halving under spatial cross-validation, and class imbalance was mitigated through controlled down-sampling. The stacked model achieved strong discrimination (AUC = 0.979, AP = 0.955) and reliable calibration (Brier = 0.054), outperforming individual learners. A geomorphic consistency analysis showed a systematic increase in susceptibility with increasing slope angle. Bootstrap ensembles with isotonic calibration were used to quantify predictive uncertainty and assess the stability of probabilistic performance across resampled training datasets. The resulting susceptibility maps are statistically robust, geomorphically consistent, and probabilistically interpretable, providing a reproducible workflow for susceptibility mapping and risk-informed planning in mountainous terrain. Presents a hybrid ensemble framework integrating heterogeneous classifiers within a stacked meta-learning architecture for high-resolution landslide susceptibility mapping. Applies spatial cross-validation and isotonic calibration to obtain robust generalization and probabilistically reliable susceptibility estimates. Quantifies predictive uncertainty using a 1,000-member bootstrap ensemble, demonstrating stable discrimination and well-calibrated performance across resampling iterations.

Using the examples of the solubility of NaCl and NaNO3 in water, the statistical treatment of all published data is compared with a selection of reports of 2 – 4 authors of particularly carefully performed and described solubility determinations. The uncertainties in the latter case are up to ten times smaller. For t < 100 °C, such accurate work was published at the end of the 19th century. The temperature, when anhydrite, CaSO4, in contact with water starts to form gypsum, CaSO4·2H2O, and vice versus can be predicted by the temperature of intersection of the solubility curves of both minerals. Exact knowledge of this temperature is of interest for the geoscience of evaporitic rocks and tunnel construction planning through sulfate-containing rocks. However, the uncertainty resulting from separate statistical treatment of the solubility data of gypsum and anhydrite an uncertainty, which is too large for fixing the temperature of gypsum/anhydrite transition within a 2–3 K range. Experimental solubility determinations with focus on the intersection temperature are superior to statistical treatments and yield a temperature of (42.1 ± 1.5) °C, which is supported by independent caloric measurements. In the system MgSO4 – H2O, a series of stable hydrates occurs along the solubility curve of magnesium sulfate in dependence on temperature. Above 68 °C, the monohydrate represents the stable phase, known as mineral kieserite, which is found in evaporitic rocks and was formed at ambient temperatures in solutions rich in MgCl2. Large amounts of magnesium sulfate hydrate on the surface of the planet Mars raise the question of whether the monohydrate represents a primary factor for water distribution control. Due to kinetic difficulties in achieving solubility equilibrium in the laboratory at low temperatures, thermodynamic modelling is applied to predict the low temperature limit for kieserite formation. It is shown that experimental evidence is still missing to confirm the model’s predictions.