Abstract Thessaly includes one of Greece’s largest plains and the watershed of Pindus Mountain range and thus is particularly susceptible to flooding. Storm Daniel caused extensive floodings in Thessaly basins, in September 2023. Significant agricultural productivity, along with relevant industrial activity, takes place in Thessaly; therefore, such intensive climate change phenomena affect not only the topical ecosystem but the national economy. In this work, Thessaly was divided into six sectors, to investigate the changes in the environmental radioactivity map caused by the Storm Daniel. Samples were collected from flooded and not-flooded locations in each sector. γ-Spectroscopy was performed on the samples with a high-purity germanium detector. The results for all the sectors show Cs-137 concentrations in the flooded areas lower than the ones in the not-flooded areas. Specifically, the mean concentration of Cs-137 in flooded and not-flooded areas is 4.6 Bq/kg and 38.8 Bq/kg, respectively. The concentrations of the other radionuclides vary across the sampled sectors, indicating the influence of flood events on the distribution of the NORM radionuclides in the soil. Remarkable differences are observed only in flooded and not-flooded areas for U-238 and Ra-226. The mean concentrations of U-238 are 18.6 Bq/kg and 24.7 Bq/kg, and the mean concentrations of Ra-226 are 21.1 Bq/kg and 28.5 Bq/kg, respectively. The uncertainty of the measurements is approximately 17% for U-238 and about 6% for all the other radionuclides. The findings contribute to the understanding of the environmental impact of floods, particularly in regions with significant agricultural and ecological value. This work suggests that floods can alter the radionuclide profile of soils. Additionally, it highlights the need for ongoing monitoring and further research to assess the long-term effects of flooding on radiological maps and the broader implications for the environment and public health.

Abstract Uniaxial compressive strength (UCS) is one of the most fundamental parameters in rock mechanics, widely used in the design and stability assessment of geotechnical and mining structures. However, its direct determination requires high-quality samples, sophisticated laboratory facilities, and significant time and cost, which often limit its applicability in practice. As a result, a broad spectrum of indirect estimation techniques has been developed, ranging from simple empirical correlations to advanced artificial intelligence (AI) models. This review provides a comprehensive synthesis of the methods employed in UCS estimation, with a particular focus on both conventional index tests and machine learning approaches. Traditional methods such as the Schmidt rebound hammer (SRH), ultrasonic pulse velocity (UPV), point load test (PLT), and Brazilian tensile strength (BTS) have demonstrated considerable utility, though their predictive accuracy is highly dependent on lithology, rock anisotropy, and site-specific conditions. On the other hand, AI-based techniques, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and optimization-enhanced hybrid models, have achieved superior predictive performance by capturing nonlinear and multivariate relationships, often yielding coefficients of determination ( R 2 ) above 0.95. Despite their promise, AI methods require large and representative datasets, and issues of model interpretability and overfitting remain challenges. The comparison highlights that no single approach is universally applicable; rather, the integration of empirical knowledge with computational intelligence appears to be the most effective strategy. The study concludes that future research should prioritize the development of hybrid models and standardized open-access databases to enhance the accuracy, robustness, and practical applicability of UCS prediction in diverse geological settings.

Abstract Routine analysis of source parameters is mainly based on the assumption that stations are located in the far field, which is valid for most measurement conditions of natural seismicity. The study aims to examine the validity of this assumption for anthropogenic seismicity characterized by shallower earthquakes and smaller distances of stations from seismic sources. Data from three real cases were studied: reservoir-triggered seismicity in Vietnam, copper mining-induced seismicity in Poland and geothermal field The Geysers. The influence of the intermediate and near fields on the values previously used to calculate source parameters was assessed. The results indicate the need to improve tools employed so far. It is especially significant in the seismicity induced by copper mines and geothermal field, even when surface stations monitor the seismicity. These effects are less important in the case of the tested seismicity in Song Tranh 2 but should not be neglected.

Abstract This paper presents a CSA-DLSQ hybrid inversion that uses Cuckoo Search Algorithm (CSA) which provides better estimation for petrophysical properties. This hybrid technique significantly reduces computational costs while preserving the global convergence properties of CSA and the fast convergence of DLSQ. Inversion is realized of depth-local petrophysical parameters and zone-dependent global variables for facilitating geological consistency in layered formations. A single-layer synthetic model is first used for investigating CSA's hyperparameter sensitivity (population size, discovery rate P a, Lévy exponent β ) and to analyze the algorithm’s dynamic search behavior. An extension to a four-layer model with global cementation exponent is then used to reduce parameter oscillations at layer boundaries. The hybrid algorithm successfully stabilizes the inversion process and converges faster than stand-alone CSA. The method is used for well-log data observed in a Hungarian borehole, resulting in a 56.6% reduction in total misfit and ~ 87 × per-point speedup, generating petrophysical parameter distributions conforming with lithological interpretation. The hybrid inversion is more accurate and efficient than stand-alone CSA, especially for zones of complex geology or noisy measurements. The results confirm that the algorithm developed offers a robust and scalable framework for petrophysical interpretation within conventional hydrocarbon reservoirs.