Acta Geophysica最新文献

Drought is a prolonged period of significantly reduced precipitation, resulting in water scarcity and environmental stress. In this study, Ağrı province, situated in the eastern region of Türkiye, where most of the land cannot be irrigated and the livelihood is based on agriculture, was selected as the study area. Meteorological droughts in Ağrı province were forecasted using hybrid machine-learning models, leveraging monthly precipitation and temperature series from 1965 to 2022. The study employed the standardized precipitation index (SPI), relying solely on precipitation data, and the standardized precipitation evapotranspiration index (SPEI), which also considers both temperature and precipitation data. Various timescales, including 1M (1 month), 3M, 6M, 9M, and 12M, were taken into consideration. The best model for each hybrid model was determined using data at time points t, t-₁, t_-2, t_-3, and t_-4 for the relevant time series. The study combined ensemble least squares boosting algorithms (LSBoosting), adaptive network-fuzzy inference system (ANFIS), support vector machines (SVM), Gaussian process regression (GPR), and M5 model tree (M5Tree) approaches with the variational mode decomposition (VMD) technique to create hybrid models. The results indicate that certain models perform better at different timescales, with M5Tree and GPR generally providing higher accuracy. For instance, the M5Tree model achieved the lowest MAE (0.0714 and 0.0555) and RMSE (0.0909 and 0.0732) values for the _9MSPI and _12MSPI timescales, respectively, making it the best-performing model at these scales. Similarly, the GPR model stood out for the _1MSPI and _6MSPI scales with the lowest MAE values (0.1336 and 0.0736, respectively). Based on the performance criteria, the best hybrid model for the _1MSPI was the GPR approach. For the SPEI, except for _3MSPEI, the M5Tree approach showed the best performance at other timescales. However, since the prediction outcomes gave similar results according to classical performance criteria, a one-sided Wilcoxon sign rank test was applied to the outcomes of ANFIS, GPR, and M5Tree models for _3MSPEI, _6MSPI, _9MSPI, and _12MSPI. It has been determined that these three models are not superior to each other. Additionally, the one-sided Wilcoxon signed-rank test found no statistically significant difference between ANFIS, GPR, SVM, and M5Tree models for the _3MSPI. This research concluded that the performance of hybrid machine-learning methods applied to different timescales of SPI and SPEI varies.

Karst groundwaters are vulnerable and essential resources that require comprehensive management for protection and preservation. For this purpose, awareness of effective factors (water quality, low pollution vulnerability, steady temperature, low susceptibility to environmental disaster and climate change) are required for the development of karst water resources and their quality management. Identifying the spatial distribution of springs in karst settings is important for a better understanding of groundwater flow because springs are the terminal sites of karst flow networks which are often understudied. This study aims to identify the location of karst spring occurrence with an emphasis on geomorphic factors using the Analytical Hierarchy Process (AHP) and Logistic Regression (LR) model. As the case studies in this research, the Lordegan and Shahrekord karst basins located in Iran’s Zagros Mountains were selected. Nine factors influencing spring occurrence are considered and classified into four major groups: geological layer (lithology and distance from fault), hydrology layer (distance from river and drainage density), geomorphological layer (slope, aspect, elevation, and plan curvature), and anthropogenic layer (land use/land cover). The occurrence map of karst groundwater spring weighed by AHP was classified into five classes (very low, low, moderate, high, and very high) and both basins were in very high to moderate class. The geological layer (i.e., lithology and distance from faults) was the most significant geomorphological factor in the Lordegan basin, with the weight of 56.3%, whereas the topographical layer (i.e., slope, aspect, elevation, and curvature) was in the Shahrekord basin, with the weight of 38.4%. Due to the high-altitude of the studied basins (1944–3297 m), the land use/land cover layer had the lowest weight.

The extraction of geothermal energy is associated with induced seismicity. Depending on the extraction parameters, such as injection pressure and volume, the induced seismicity is time-dependent. We investigate the case of the two geothermal power plants of Insheim and Landau, which are located in the Upper Rhine Graben in Southwest Germany. The induced seismicity was observed by a local seismic network consisting of a total of 19 stations, resulting in an earthquake catalog comprising 930 events for the Landau reservoir and 1985 events for the Insheim reservoir, both between 2012 and 2022. Based on this earthquake catalog, seismic source areas are defined for both reservoirs, and a probabilistic seismic hazard assessment (PSHA) is performed. Using temporal subsets of the earthquake catalog, PSHA can also be performed for shorter time ranges, resulting in larger expected PGV values in times of higher induced seismicity. The seismic velocity profiles obtained by site effect studies based on ambient seismic noise measurements highlight relatively large variations of the site effects on short scales in the area. The consequences of these lateral variations on the seismic hazard assessment are also discussed.

Floods are among the most widespread and devastating natural disasters, accounting for 47% of all weather-related events and affecting over 2.3 billion people, particularly in Asia. Assessing flood-prone areas is crucial for effective disaster risk reduction, but existing flood damage estimation methods, such as depth-damage functions, often lack regional adaptability and accuracy. This study addresses this gap by integrating geospatial data, remote sensing, and artificial intelligence (AI) to identify flood-affected areas in the Kan basin, Tehran. We applied deep learning methods, specifically U-Net and fully convolutional neural network (FCN) algorithms, to optical and radar images from four flood events. Our results demonstrate that the U-Net model achieves significantly higher accuracy (88%) in identifying flood-affected areas compared to the FCN model (55% accuracy). This superior performance is further supported by the mean intersection over union (mIoU) values, with U-Net achieving 0.65, compared to 0.55 for FCN. The key message of this investigation is that deep learning, particularly the U-Net model, applied to remote sensing data holds significant promise for enhancing flood monitoring, early warning systems, and disaster management strategies by enabling more accurate and timely flood assessments.

The identification of adverse geological bodies in front of the tunnel excavation face is essential to allow precautionary measures to be taken for safer tunnel construction. Currently, the interpretation of seismic data in tunnels relies more on experience, which is the result of a lack of understanding of the seismic wave propagation mechanism in tunnels. Therefore, on the basis of the finite element method of seismic waves in two-phase media, we investigated the time-domain evolution characteristics of seismic waves in tunnel spaces with two typical adverse geological bodies, namely, water-bearing fracture zones and lithological interfaces. The results show that under the combined influence of solid–solid and solid–fluid phase reflections, the propagation complexity of the seismic wave significantly increases, and the obvious diffraction phenomenon caused by the tunnel face plays a promoting role. The amplitude attenuation of the waves obtained from the self-defined factor is exponentially related to the propagation time, which is influenced by the energy difference of the wave itself and the degree of wave cross talk in the propagation process. This study can provide theoretical guidance for the technological development and field application of tunnel geological detection.

An investigation was conducted to determine radon concentrations, radon exhalation rate, and potential radiological hazard parameters associated with cement collected from five factories in Sulaymaniyah city, Kurdistan region, Iraq. Using solid-state nuclear track detectors such as CR39, the samples were analyzed by etching processes. The average radon concentration, radium concentration, and radon exhalation rate were 138.16 ({text{Bq}},{text{m}}^{ - 3}), 0.254 ({text{Bq}},{text{kg}}^{ - 1}), and 0.317 ({text{Bq}},{text{m}}^{ - 2} ,{text{h}}^{ - 1}), respectively. In sample 14, radon concentrations were within the suggested range of 200–600 ({text{Bq}},{text{m}}^{ - 3}), and the radon exhalation rate was well below the global average of 57.600 ({text{Bq}},{text{m}}^{ - 2} ,{text{h}}^{ - 1}). In addition, parameters related to potential radiological hazards were calculated for cement samples, the average annual effective dose indoor and outdoor were 3.49 and 1.31 ({text{mSv}},{text{y}}^{ - 1}), so this study's value was within the global average limitations (1–5 ({text{mSv}},{text{y}}^{ - 1})). Also, the excess lifetime cancer risk indoor and outdoor were 12.5 × 10⁻³ and 4.69 × 10⁻³ greater than the world value of 0.29 × 10⁻³.

Streamflow changes in basins are influenced by two significant factors: anthropogenic activities and climate change (AACC). Separating the attribution of each factor is crucial for managing water resources and economic, political, and social activities. In this study, the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model is used to separate annual AACC attributions to streamflow changes in Iran's Karkheh basin. The trend-free pre-whitening Mann–Kendall (TFPW-MK) test is used to determine trends in annual precipitation, streamflow, and air temperature. Multivariate dependence analysis is performed for precipitation and streamflow changes and attributions of AACC to streamflow changes using different copula functions. The point of change in the annual streamflow series, identified by the double cumulative curve (DCC) and Pettitt test, occurred in 1999. Compared to the pre-change period, the average annual streamflow decreased by 42.3%. The results of the hydrologic model simulation showed that climate change and anthropogenic activities contributed to streamflow reduction by 36.9% and 63.1%, respectively. The results showed that the attributions of AACC to streamflow each year could be obtained based on the dependence analysis between precipitation changes, streamflow changes, and the attributions of AACC to streamflow changes with copula functions. The results showed that for a joint probability of 0.5, the values of attributions of AACC to streamflow with the maximum joint density are equal to −42 and −36 mm, respectively.

Geometric analysis of hydraulic fractures in unconventional shale oil reservoirs is essential for enhancing production efficiency. This paper proposes a new method for evaluating large-scale fractures using cross-borehole electromagnetic measurement technique based on numerical simulation. The three-dimensional finite element method (3D FEM) is used to establish a hydraulic fracture model of horizontal wells, and the accuracy and validity of algorithm are verified using a benchmark model. The relationships between the geometric parameters of fractures and the obtained measurement signals are investigated. To evaluate the effectiveness of our proposed method in complex underground conditions, a case study is conducted. Numerical results indicate that the coaxial component signal (xx,yy,zz) is effective in characterizing hydraulic fractures. The signals exhibit greater sensitivity to the T-R spacing, fracture conductivity, fracture half-length, and fracture number, compared to transmitter frequency and fracture aspect ratio. Furthermore, the opening angle of asymmetrical fractures should be wider than 120° to ensure proper fracturing. In the segmented fracturing monitoring case study, positioning the transmitter source inside the fracturing borehole greatly aids in determining the orientation of fractures, while deploying it in the monitoring borehole conveniently improves the collection of response signals with a more prominent amplitude. This study demonstrates that the cross-borehole measurement method is an effective technique for monitoring hydraulic fracturing in open boreholes and offers promising applications.

Many coastal regions around the world are experiencing significant groundwater over-exploitation, necessitating scientific studies to prevent seawater intrusion into coastal aquifers. Accurate assessment of groundwater variation is essential for improved water resources management in these regions. Trend analysis of groundwater levels along the coast of Kerala, India is conducted in this study to identify the most critical areas susceptible to seawater intrusion. The study reveals that Ponnani in Malappuram district, Kerala is particularly susceptible to seawater intrusion. The hydro-chemical characteristics were analyzed to understand the groundwater properties. Groundwater modeling was performed using SEAWAT, which reveals that the Ponnani region is affected by seawater intrusion and the modeled length of intrusion has been found to be 2.5 km during the year 2009. The precipitation data from downscaled GCM was employed to estimate future recharge and the simulated length of intrusion in 2030 under Shared Socioeconomic Pathways 5-8.5 (SSP 5-8.5) has been found to be 1.6 km.

An electromagnetic (EM) logging tool with tilted-coil antennas is essential in geoelectrical resistivity measurement for its enhanced capability to discern formation dip, azimuth, and anisotropy, surpassing the capabilities of axial-coil antennas. However, interpreting their measurements in logging-while-drilling is notoriously challenging, often plagued by multi-solution issues and time-consuming processes. As a result, aiming at realizing fast inversion for EM logging tools with titled-coil antennas in a layered vertical transverse isotropy media, this paper proposes a general framework assisted by deep learning. Firstly, a deep neural network (DNN) architecture integrates with adaptive moment estimation and exponential moving averages to enhance the model’s performance. Then, the accuracy of the proposed fast inversion method is validated through several experiments, where different types and levels of noise are introduced to the measurements to test the robustness of the proposed inversion scheme. Finally, the effectiveness of the proposed approach is proved by comparing the inversion scheme with the traditional inversion method in the same scenarios. This study concludes that DNN-assisted inversion can reconstruct subsurface formations in real time and overcomes the limitation of nonlinear iterative algorithms, which typically depend on initial value estimates.