Acta Geophysica最新文献

This study focuses on developing region-specific seismic attenuation relationships for North-west Iran, a tectonically active area prone to frequent and destructive earthquakes. By analyzing a robust dataset of seismic events, we identify breakpoints in attenuation behavior at distances of 85 km and 175 km, attributed to crustal features such as the Moho and Conrad discontinuities. Using nonlinear optimization and inversion methods with explicit parameter bounds, we estimate frequency-dependent parameters, including geometric spreading coefficients, quality factor (Q), and magnitude-dependent terms. The geometric spreading coefficients for velocity data show slight variations across frequencies, reflecting the complex crustal structure in the region. Negative values of these coefficients indicate a significant velocity contrast at the Moho discontinuity, leading to substantial energy reflection. The observed amplitude decay trend remains consistent between breakpoints, with a notable change at approximately 175 km, likely due to the superposition of reflective phases from the Conrad and Moho discontinuities and multiple reflections within the S-wave window. Crustal stratification ensures continuous energy reflection, resulting in geometric spreading attenuation coefficients that exceed theoretical predictions. These empirically derived coefficients are intended for regional hazard assessment and may not be directly portable to other tectonic settings. The calculated average shear wave quality factor (Q) for the region is empirical and reflects the area’s structural characteristics and high seismicity. The findings provide practical insights for seismic hazard assessments and support the design of resilient infrastructure in North-west Iran.

A bridge abutment is the most critical civil engineering infrastructure directly exposed to floodwater. Numerous studies have been conducted to mitigate scour around bridge abutments; however, limited research has focused on assessing flow dynamics and energy reduction around bridge abutments using eco-friendly methods. Therefore, the current research investigates energy reduction and flow dynamics around bridge abutments with recycled materials (brick waste (BW) and marble waste (MW)) under subcritical flow conditions. Experiments were conducted in a controlled laboratory setting to investigate various parameters, including water surface profile, energy reduction, reduction of fluid force index (RFI%), moment index (RMI%), and delay in floodwater arrival time. These parameters were investigated under different conditions, including without waste (WW) and with recycled materials. The result demonstrates that energy reduction increases as the Froude number (Fr) is increased from 0.13 to 0.22. Energy reduction increases up to 5.95, 6.5, and 6.27% in the case of WW, MW, and BW, respectively. The use of MW resulted in a maximum energy reduction, with an average energy reduction of 4.38%. The highest RFI% of 8.86% and RMI% of 12.44% were recorded when using MW during the experiments. The findings also show that a significant reduction in floodwater arrival occurred in the case of MW up to 68% compared to the case without an abutment in the channel. These findings offer valuable insights into the flow characteristics and energy dissipation around bridge abutments, thereby contributing to the design of sustainable and resilient hydraulic infrastructure.

Sustainable water management is particularly important in mountainous areas, where access to surface water is limited and drilled wells often remain the only reliable source of fresh water. Locating aquifers in such regions is challenging due to the complex geological conditions. In this context, geophysical methods, especially electrical resistivity tomography (ERT), can provide valuable support in identifying zones with higher groundwater potential in areas such as the Carpathian flysch, composed mainly of sandstones and shales occurring in varying proportions. The paper presents case studies from the Magura and Silesian Nappes, demonstrating how ERT surveys, verified by borehole data, helped indicate aquifer locations and assess hydrogeological conditions. The application of ERT in the specific geology of the Carpathian flysch allowed for the identification of the influence of lithological proportions and water mineralisation on the values of electrical resistivity and the summary of the limitations and possibilities of the ERT method in difficult mountain conditions.

Although heterogeneous geological settings may limit the precision of interpretations, the results confirm that ERT is an effective tool for improving the recognition of groundwater resources in mountainous flysch areas and thus giving people access to water.

Extensive research has been conducted on the effects of anthropogenic practices on lowland rivers and floodplains; particularly regarding planform changes, only a few studies have utilized detailed riverbed elevation data. This study focuses on the Apalachicola River, one of the largest lowland rivers in the southeastern United States. The navigation project by the United States Army Corps of Engineers, which began in the late 1950s and continued till 2002, significantly impacted the Apalachicola River. The dredging and disposal, artificial cutoffs, and snag removal carried out as part of the navigation efforts significantly altered the Apalachicola River. Using bathymetric survey data from 1960 to 2010, we developed a high-resolution digital elevation model (DEM) to analyze geomorphic changes in the lower Apalachicola River and conduct a DEM of differences analysis for the 50-year period. We assessed the river’s net sediment gain and loss patterns using the DEMs and geostatistical approaches. We quantified the cumulative sediment volume change and gross change (cumulative absolute change) per river mile of the lower Apalachicola River between 1960 and 2010. The study revealed that the entire reach (RM ~ 45-0) experienced a loss of 8.36 million m³, a gain of 6.99 million m³, a gross change of 15.35 million m³, and a net change of 1.37 million m³. The reach upstream of the juncture with the Lower Chipola (~ RM 28), where several artificial cutoffs were present, experienced a net loss of 4.52 million m³. In this reach and just downstream of the juncture between RM 30 and 27, multiple pools deepened markedly. These morphological alterations have significantly compromised natural river–floodplain connectivity and altered critical aquatic habitats, particularly affecting the spawning and nursery areas essential for the region’s diverse freshwater mussel populations and other endemic species. However, downstream of RM 28, the Apalachicola had a net gain of 3.14 million m³, probably associated with sediment supply from downcutting and lateral erosion occurring upstream. This comprehensive sediment budget analysis provides essential quantitative evidence for river managers and restoration practitioners, demonstrating that navigation-induced modifications can redistribute over 15 million m³ of sediment across a 45-mile reach, with direct implications for habitat restoration planning, flood risk assessment, and sustainable waterway management in similar modified lowland river systems globally.

Accurate prediction of formation fracturing pressure is crucial for drilling safety and reservoir protection. In this study, a long short-term memory (LSTM) neural network model integrated with geomechanical constraints (Physical-LSTM) is proposed, which achieves deep coupling of data-driven approaches and physical laws through a physical constraint correction layer and a multi-objective loss function. Based on logging-while-drilling and drilling data from three wells in the Bohai Sea area, 15 key parameters were selected as the model inputs. The physical constraints include: the fracturing pressure must be greater than the pore pressure, less than the overburden pressure, and monotonically increasing with well depth. Bayesian optimization was employed to determine the weights of physical constraints and data fitting ((alpha) = 0.8, (beta) = 0.6). The experimental results show that the Physical-LSTM model achieves a mean squared error (MSE) of only 0.0015, a mean absolute error (MAE) of 0.0261, a coefficient of determination (R²) of 0.981, and a normalized Nash–Sutcliffe efficiency (NNSE) of 0.978 on the test set, which is significantly superior to the baseline models including LSTM, GRU, LightGBM, XGBoost, and RF. Compared with the traditional Eaton model, the Physical-LSTM not only maintains consistency in prediction trends but also substantially reduces prediction errors and eliminates physically unreasonable outliers. This study confirms that embedding physical constraints into machine learning models can significantly improve the accuracy, physical rationality, and engineering reliability of formation fracturing pressure prediction.

As critical components of hydraulic structures, radial gates experience complex flow patterns during operation, inducing hydrodynamic loads that may threaten structural stability. This study investigates the flow characteristics around the radial gates under different conditions by using Particle Image Velocimetry (PIV) test in a laboratory flume. It is found that three key zones emerged behind the gate: a high-velocity jet zone, a shear layer marked by a velocity gradient, and a recirculation zone with reverse flow. The downstream water depth critically controls the evolution of these flow zones. Turbulence intensity peaks within the jet zone, decaying progressively across the shear layer. The flow self-similar is exhibited in the far-field region. Energy analysis reveals that large-scale vortex structures govern the kinetic energy distribution. These findings enhance our understanding of flow regimes near radial gates and support the optimization of gate designs for improved stability.

Three different ground motion prediction models (GMPMs) have been developed in this paper using machine learning (ML) methods to estimate the horizontal peak ground acceleration (HPGA) for Iran. Two of these models are based on artificial neural networks (ANNs) of the multilayer perceptron (MLP) type, while the third employs the support vector regression (SVR). Each model utilizes moment magnitude (M_w), fault type, epicentral distance, and soil type as features (predictors) to produce a numerical prediction for HPGA. The models have been trained, validated, and tested using a strong-motion dataset comprising 2472 corrected horizontal accelerograms from 1100 earthquakes recorded at 815 stations across Iran from 1974 to 2022. Given the significant imbalance in the number and magnitude of recorded accelerations for Iran, an algorithm called the Repeating function has been devised to mitigate this problem within the training dataset. Besides, we designed an innovative training loop that automatically trains a model multiple times until specified criteria for the model are confirmed. Notably, three developed ML models (DMLMs) accurately predict HPGA, even in cases where V_S30 is not defined. Although we have trained the three DMLMs to predict HPGA as the maximum value of the two horizontal components of the accelerogram (HPGA_max), they demonstrate a strong generalization in predicting the arithmetic and geometric means of the two mentioned components (HPGA_am and HPGA_gm). To evaluate the performance of the models, sensitivity and residual analyses, fitting curves, root-mean-square error (RMSE), and Pearson correlation coefficient (PCC) have been conducted.

This study uses deep learning models to present an advanced methodology for forecasting groundwater levels. The primary objective is to estimate monthly streamflow at various gauging stations, analyze long-term groundwater storage trends from 1986 to 2022, and predict future groundwater storage (GWS) for 2028. The majority of research relies on single-model forecasts, without considering regional hydrological variability or integrating minimal-data contexts, despite the increasing use of deep learning models in hydrology. By employing an ensemble deep learning (DL) architecture that combines Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Stacked Long Short-Term Memory (SLSTM), and Gated Recurrent Unit (GRU), this study closes that gap by accurately predicting groundwater storage over the Middle Mahanadi Basin utilizing Hargreaves–Samani potential evapotranspiration (PET) estimate and SCS-CN runoff. Results reveal that the Ensemble DL model consistently outperforms individual models across all gauging stations, offering the most accurate predictions of GWS changes. This model’s integration of multiple techniques allows it to capture complex patterns and mitigate errors, particularly in regions with high variability. The analysis of seasonal trends reveals that the post-monsoon season exhibits increased groundwater storage, whereas the pre-monsoon and monsoon seasons display a declining trend. In 2004, there was a decrease in GWS across most stations out of 8 stations, likely due to reduced rainfall and increased water extraction, with slight recoveries observed in 2016 and 2022. In conclusion, the Ensemble DL model emerges as the region’s most reliable tool for groundwater forecasting, offering valuable insights for effective water resource planning and management, particularly in drought-prone areas. In drought-prone basins with limited data, the model provides a dependable tool for groundwater management and performs better than individual DL models at every station.

Adjustment of methane adsorption and desorption properties in coal is important for the high drainage rate and drainage effect of coalbed methane (CBM). The main characteristics of CBM reservoirs in the Qinshui coalfield of Shanxi Province are low pressure and low permeability of CBM. These conditions result in limited methane extraction and significant fluctuations in gas concentration. Revising the adsorption and desorption properties of CBM can improve both the extraction rate and efficiency. An attempt was made to revise the adsorption and desorption characteristics of methane in coal by adding an electric field. This study focuses on No. 3 anthracite in the southern Qinshui coalfield. Elemental analysis was conducted, and an electric field was used as a physical field to develop an experimental apparatus for electric field-revised CBM adsorption and desorption. This apparatus was used to test the adsorption properties of CBM, and X-ray photoelectron spectroscopy (XPS) was employed to examine the surface chemistry of coal. The types and relative contents of functional groups on the coal surface were analyzed. Additionally, the relationship between the electric field’s influence on CBM adsorption and the changes in functional groups on the coal surface was investigated. The experimental results indicate that the application of an electric field changes methane adsorption from coal, adhering to the Langmuir theory model. The impact of voltage on methane adsorption capacity and adsorption isotherms is greater than that of frequency. Moreover, as the intensity of the electric field increases, the maximum adsorbed quantity V_m demonstrates a linear decrease while the empirical adsorbed constant B exhibits an exponential decline. The functional groups on the coal surface primarily include C–C/C–H bonds, C-O bonds, C = O carbonyl groups, and COO- carboxyl or quinone groups. Under the influence of the electric field, the functional groups on the coal surface are modified. The relative content of C–C/C–H bonds decreases, resulting in an increase in the relative content of C–O bonds, C = O carbonyl groups, and COO- carboxyl or quinone groups. The findings revealed that electric field action diminished the methane adsorption capacity in coal.